Triple
T35753349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belgian Grand Prix |
E1033373
|
entity |
| Predicate | hasSafetyCarLikelihood |
P188473
|
FINISHED |
| Object | high |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: high | Statement: [Belgian Grand Prix, hasSafetyCarLikelihood, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSafetyCarLikelihood Context triple: [Belgian Grand Prix, hasSafetyCarLikelihood, high]
-
A.
safetyCarPossible
Indicates that conditions are such that deploying a safety car is a valid or allowable option.
-
B.
safetyCarFrequency
Indicates how often a safety car is deployed or appears within a given context or time frame.
-
C.
safetyCarUsed
Indicates that a safety car was deployed and used during an event or activity.
-
D.
safetyCarModel
Indicates that one entity is the specific model designation of a safety car used in racing or controlled driving conditions for the other entity.
-
E.
virtualSafetyCarDeployments
Indicates the number of times a virtual safety car period has been deployed during an event or session.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fba78aca4c8190b8f1831e8cc04e06 |
completed | May 6, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69fba34a65a4819088bac6c17542d71c |
completed | May 6, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69fba789c1188190973a919bfe2871f3 |
completed | May 6, 2026, 8:41 p.m. |
Created at: May 3, 2026, 4:06 p.m.